Path to Azure AI Engineer Certification — Tips, Insights, and AI-102 Exam Experience
AI skills have become some of the most sought-after talents today. Getting hands-on with AI technologies and demonstrating your competencies in building AI apps can be a significant career booster for developers in the current landscape.
There are various ways to build and showcase your skills — including earning an industry-recognized credential. In this regard, Microsoft’s “Azure AI Engineer” certification, which includes passing the AI-102 exam, is an excellent starting point for building your career and capitalizing on the AI wave.
I recently passed the AI-102 exam and earned Microsoft’s “Azure AI Engineer” certification! I thought it would be helpful to share my thoughts, experiences, and strategies I adopted while preparing will be beneficial to anyone who is currently considering taking the exam.
First of all, why should we learn and upskill in AI?
In today’s world, businesses are racing to build AI-powered applications or leveraging AI capabilities to optimize their operational effectiveness and efficiency. This surge in interest is largely due to OpenAI’s ChatGPT, which seemed to take the world by storm back in November 2022.
This breakthrough sparked a frenzy of excitement and curiosity about how to harness this newfound power and capitalize on it. Now, we see countless startups using AI technologies to tackle problems that weren’t even on the radar a few years ago, while large organizations strive to reap the benefits by optimizing their operations with AI. It’s like the modern-day gold rush, with everyone eager to stake their claim!
While the drive toward machine-driven autonomy might raise concerns about job security, it also pressures us — engineers and solution architects — to sharpen our skills and stay ahead of the wave.
The ability to build AI and ML-powered solutions, especially in the rapidly growing field of generative AI, has become one of the most in-demand skill sets in IT and business technology. Many organizations are already working on or considering building new business applications, adding smart capabilities to existing ones, or optimizing old business processes with AI.
Moreover, many startups and organizations are exploring how to adapt AI or build on top of pre-built AI services and technologies. Therefore, becoming familiar with the core concepts and learning how to leverage these ‘pre-built’ AI services — especially those developed on major platforms like Azure, AWS, or Google — can open up a range of new career opportunities. And who knows? You might just strike gold!
Now, about the Certification
The “Azure AI Engineer Associate” certification is a role-based credential designed to equip you with the knowledge and hands-on skills needed to build, deploy, secure, and manage end-to-end AI solutions using Microsoft Azure’s AI services. These Azure AI services include both out-of-the-box capabilities and customizable options, providing a blueprint for tailoring AI solutions to meet specific business needs.
By mastering the objectives of this certification, you’ll gain a solid understanding of Azure’s AI capabilities and how to leverage them in your applications and solutions.
You don’t need prior experience or proficiency in building AI solutions on Azure to prepare for this exam. You can start as a complete beginner. However, if that’s the case, keep in mind that you’ll need to experiment with the services yourself and complete the lab exercises provided by Microsoft (in the official MS Learn course). Some of the exam questions I encountered focused on practical implementation and configuration, so memorizing everything won’t be practical and would serve no purpose.
Overview of the AI-102 Exam
The AI-102 exam is designed to assess your understanding of the concepts, capabilities, and limitations of each Azure AI service offering, including the constraints of individual SKUs. It also tests whether you’ve learned how to effectively combine these services in complementary ways, sometimes requiring you to build scenarios that utilize more than one Azure AI service.
The exam evaluates your familiarity with the provided SDKs and REST APIs, so some of the questions will include code samples. Although there’s no live coding or labs during the exam, you might be asked to interpret what the code is intended to do or to fill in the blanks in a given snippet.
The exam typically contains between 40–60 questions, although the exact number can vary. The question formats include multiple choice, multiple response, ordering, and case study-based questions. (I got 54 questions, including 6 from a case study)
To pass, you need to score at least 700 out of 1000. Each question has a specific weight, and your final score reflects your overall performance on the exam. However, it’s not possible to determine how many questions you answered correctly based solely on your final score.
Exam Content
I won’t delve into the specifics of the exam content or outline since the official study guide is a far better resource for that. However, it’s essential to pay attention to how the exam questions are distributed across each skill area, as indicated by the overall percentage.
Plan and manage an Azure AI solution (15–20%)
Implement content moderation solutions (10–15%)
Implement computer vision solutions (15–20%)
Implement natural language processing solutions (30–35%)
Implement knowledge mining and document intelligence solutions (10–15%)
Implement generative AI solutions (10–15%)
Number of Questions and their Complexity:
The number of questions you receive from each area will likely depend on this distribution. From my experience, some questions spanned multiple skill areas, combining them into a single query.
Also, keep in mind that not all questions are equal in complexity, which is standard for most technical exams. The exam might cover certain topics or skill areas differently, depending on their complexity. For example, I encountered some skill areas with a higher number of relatively easy questions and others with fewer questions but significantly more complex ones. Additionally, at the start of the exam, there’s a survey where you rate your experience in the skill domains (in this case, the six domains under the AI-102 curriculum) as ‘Beginner,’ ‘Intermediate,’ ‘Proficient,’ etc. Though I don’t believe the exam questions are adjusted based on this (just my assumption), as by this point in the exam onboarding process, you’re already informed of the total number of questions you’ll face (54 in my case).
Not the right choice if you’re looking for ML credentials:
One key point about the topics covered in this exam is that it focuses on the core concepts of AI, the portfolio of Azure AI service offerings, and how to coordinate them to build intelligent applications that serve business purposes.
This means the exam is more about building, deploying, and managing AI-driven solutions using ‘pre-built’ Azure AI capabilities. Some Azure AI services allow you to customize the underlying model using your own data set, enabling you to tailor the AI service to better align with your business needs. However, you won’t need to deal with the aspects of developing machine learning models, such as data preprocessing, hyperparameter tuning, training, model evaluation, deployment, and maintenance. Those aspects of AI/ML development and MLOps are not covered in this certification. If you’re looking to enhance your knowledge in that side of the spectrum, the “Azure Data Scientist (DP-100)” certification would be a better choice.
Of course, some experience with Azure will be handy:
Also, most of the learning materials I found and the subsequent exam questions didn’t directly probe other non-AI service offerings of Azure (perhaps with the exception of Entra and KeyVault). However, don’t let that fool you into thinking that knowing basic Azure cloud concepts and resources isn’t essential for your preparation. While you don’t need to be an experienced Azure developer or administrator, this certification does require a solid understanding of basic concepts, tools, and services within the Azure ecosystem. For example, if you’re not familiar with navigating the Azure portal, provisioning resources, or understanding core concepts like Azure resource groups, regions, networking, and storage solutions, it’s advisable to cover those topics at least at a high level.
As I mentioned earlier, this exam will include several coding and configuration-related questions — not that you’ll need to write code on the spot, but you’ll need to review code or configuration snippets, understand them, determine if they’re correct, or fill in missing pieces. Therefore, it’s crucial to build proficiency in coding, implementing, and configuring AI solutions on Azure.
Choice of Language:
Currently, you have the choice between C# and Python as your preferred coding language (at the start of the exam, you’ll be prompted to choose the language you’re most comfortable with, and the coding questions will be based on that selection). The language options are currently limited to these two due to SDK support, but in the real world, beyond the exam, Azure AI services offer full REST API capabilities. This gives you the freedom to build solutions using any language or platform that supports integration with a REST endpoint.
Learning Materials
During my preparation for the certification, I used the following learning materials:
Scott Duffy’s Exam Prep Course on Udemy (Paid):
This course is helpful, but be aware that only about half of the content applies to the current version of the exam (the Azure AI Engineer curriculum underwent a significant overhaul in July 2024). Scott clearly marks out the outdated content, so you can focus on the relevant modules. The course provides a good overall understanding of the targeted skills, but it has some limitations — it doesn’t cover many coding examples and sometimes lacks depth in specific low-level details or configurations. This is understandable for a time-bound video lecture series, so be sure to supplement it with official materials.
MS Learn Self-paced Course (Free):
This is completely free and probably the best available resource for detailed information. While most of the modules cover concepts in detail, make sure to complete a substantial amount of lab exercises to gain hands-on experience (unless you already have prior expertise in the area).
However, one thing I noticed is that the effort required to cover each section in this learning path is disproportionate compared to the exam’s skill area breakdown as outlined in the study guide.
For example, “Knowledge Mining and Document Intelligence” is supposed to cover only 10–15% of the exam, but the MS Learn content requires roughly 30–35% of your effort.
Similarly, the “Content Moderation Solutions” skill area, which also accounts for 10–15% of the exam, hardly amounts to any effort in the learning path.
John Savill’s YouTube Content (Free):
John offers a valuable series of free video lectures. Among his many videos covering various Azure AI services, I recommend his “AI-102 Study Cram” and “Learning About Generative AI.”
However, note that some of the AI-102 content in the study cram video is slightly outdated — it still refers to the older version of Azure AI services, “Cognitive Services” and includes references to retired services like “Form Recognizer” and “Bot Service”.
Official MS Practice Test (Free):
I used Microsoft’s official practice test to gauge my readiness. However, the questions are nowhere near as complex as those on the actual exam. MeasureUp tests seem like a much better choice, but I only tried the free sample questions and didn’t purchase the full set.
In addition to the above, the official product documentation on MS Learn is always at your disposal.
How I Approached Learning
I began my study by analyzing the core skills and subtopics outlined in the exam study guide. From there, I created a study plan, estimating how many days I would need to allocate to each area. My approach was to dedicate 45 minutes to an hour each day to make steady progress. This realistic approach helped me balance work, personal commitments, and continuous education. Consistent, incremental study sessions are more manageable on a workday and are easier to stick to than sporadic, intense study bursts.
Next, I allocated rough timelines for each high-level section of the exam content. This step is crucial to keep the entire initiative on track. When determining the time needed for each topic, I based it on two factors: 1) the complexity of the particular skill area or concept, and 2) the percentage given to each subject area in the exam skill distribution.
Weight given to a certain topic in exam might not always reflect on the study material, For example, from my opinion, “Implement knowledge mining” (related to Azure AI Search, indexing, skillsets, projections, knowledge stores, etc.) introduces many concepts and jargon, requiring significant time to cover. However, “Implement knowledge mining and document intelligence solutions” section in the exam (including Document Intelligence on top of the prior) is only worth 10–15% of the overall exam questions.
Similarly, content moderation is only briefly covered in most learning materials, yet it also accounts for 10–15% of the exam spread. Since several content safety safety mechanisms appeared in questions (based on my experience), referring to the official product documentation for that section is crucial.
When creating your study schedule, I recommend keeping these considerations in mind to avoid getting lost in the details and losing focus. However, while you should balance your workload when preparing for the exam, you should never ignore essential areas — just be mindful of the depth you’re delving into.
My 5-Step Preparation Plan
Step 1: I quickly reviewed John Savill’s “AI-102 Study Cram” and “Learning about Generative AI” content on YouTube to get an overview without getting bogged down in details. His use of a single smart board diagram to explain and connect concepts was particularly helpful.
Step 2: I worked through the MS Learn Self-paced Course, taking my time and completing almost all the lab exercises. (excluding the Open AI lab, as I was not able to get the approval from Microsoft to try it out on my personal subscription without providing any company information). This provided a more in-depth understanding. (After this point, I actually took the AI-900 exam just to measure my understanding of the fundamental concepts).
Step 3: After finishing the MS Learn course, I used Scott Duffy’s course to recap and review my understanding.
Step 4: I attempted practice tests from Microsoft, as well as free sample questions from MeasureUp. However, later I found that Microsoft’s official practice tests were nowhere near to the actual exam questions in terms of complexity.
Step 5: Finally, I reviewed areas where I made mistakes, going back to the official study guide documentation. For any concepts I was unsure about or got wrong on practice tests, I referred to the official product documentation for a deeper understanding.
In my opinion, this final step — revisiting official documentation to fill knowledge gaps based on the study guide and practice test results — was incredibly productive. I highly recommend it! I only did this in the last day or two of my preparation and regret not allocating more time to it earlier in the process.
A Few Tips from My Experience
MS Learn Can Become a Rabbit Hole if You’re Not Careful — You can access certain parts of MS documentation during the exam, but most questions don’t ask for specific features or capabilities. Instead, they test your understanding and ability to apply these concepts in real-world scenarios. Therefore, there’s no direct way to “look up” answers in the documentation (don’t expect it to be that easy!). Use MS Learn primarily for things like, recalling exact names of SDKs/REST methods or specific service limits. Avoid getting sidetracked by trying to ‘explore’ the documentation to find answers for less direct questions- resist the rabbit hole!
If you need to look up specific information from MS Learn, navigate via the left menu rather than using search functionality. Search usually list out a large number of results, which will make it way more difficult to figure anything out. Navigating via the menu will help you quickly locate content. The above ‘Step 5’ is a valuable exercise as it helps you familiarize with where to look for.
While studying, pay attention to MS Learn Tips. These tips often provide key insights into edge cases, boundary conditions, or limitations that can be crucial for some exam questions.
I encountered several questions related to specific service options, tiers, and limits for Document Intelligence, Content Safety, and Generative AI. However, I didn’t experience similar questions for other AI services.
Don’t Get Stuck on a Question. Remember, you can review your answers except when moving between different exam sections (e.g: from case studies to normal questions), where you won’t be able to revisit previous sections.
When tackling case studies, you’ll often find a lot of information spread across multiple tabs, which can be overwhelming. My approach was to read through the case study once to grasp the high-level scenario and then revisit the information based on the scope of each individual question, focusing on specific details as needed.
I encountered several questions about securing and authenticating AI applications, as well as different networking and segmentation approaches for securing AI apps.
Hope this helps! All the best on your studies and exam preparations!
Disclaimer
This content is based on the exam content and the official study guide as of August 25, 2024. The certification curriculum, exam content, and MS Learn/product documentation may change based on new services or updates to the exam by Microsoft.
